OpenRaiser/Intern-Atlas huggingface dataset:
Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning
Venue: arXiv cs.CY 2019
Year: 2019

The ongoing Yemen cholera outbreak has been deemed one of the worst cholera outbreaks in history, with over a million people impacted and thousands dead. Triggered by a civil war, the outbreak has been shaped by various political, environmental, and epidemiological factors and continues to worsen. While cholera has several effective treatments, the untimely and inefficient distribution of existing medicines has been the primary cause of cholera mortality. With the hope of facilitating resource allocation, various mathematical models have been created to track the Yemeni outbreak and identify at-risk administrative divisions, called governorates. Existing models are not powerful enough to accurately and consistently forecast cholera cases per governorate over multiple timeframes. To address the need for a complex, reliable model, we offer the Cholera Artificial Learning Model (CALM); a system of 4 extreme-gradient-boosting (XGBoost) machine learning models that forecast the number of new cholera cases a Yemeni governorate will experience from a time range of 2 weeks to 2 months. CALM provides a novel machine learning approach that makes use of rainfall data, past cholera cases and deaths data, civil war fatalities, and inter-governorate interactions represented across multiple time frames. Additionally, the use of machine learning, along with extensive feature engineering, allows CALM to easily learn complex non-linear relations apparent in an epidemiological phenomenon. CALM is able to forecast cholera incidence 2 weeks to 2 months in advance within a margin of just 5 cholera cases per 10,000 people in real-world simulation.
Finding Robust Itemsets Under Subsampling
Venue: arXiv cs.DB 2019
Year: 2019

Mining frequent patterns is plagued by the problem of pattern explosion making pattern reduction techniques a key challenge in pattern mining. In this paper we propose a novel theoretical framework for pattern reduction. We do this by measuring the robustness of a property of an itemset such as closedness or non-derivability. The robustness of a property is the probability that this property holds on random subsets of the original data. We study four properties: if an itemset is closed, free, non-derivable or totally shattered, and demonstrate how to compute the robustness analytically without actually sampling the data. Our concept of robustness has many advantages: Unlike statistical approaches for reducing patterns, we do not assume a null hypothesis or any noise model and in contrast to noise tolerant or approximate patterns, the robust patterns for a given property are always a subset of the patterns with this property. If the underlying property is monotonic, then the measure is also monotonic, allowing us to efficiently mine robust itemsets. We further derive a parameter-free technique for ranking itemsets that can be used for top-$k$ approaches. Our experiments demonstrate that we can successfully use the robustness measure to reduce the number of patterns and that ranking yields interesting itemsets.
Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots
Venue: arXiv cs.RO 2019
Year: 2019

Mobile robots need to create high-definition 3D maps of the environment for applications such as remote surveillance and infrastructure mapping. Accurate semantic processing of the acquired 3D point cloud is critical for allowing the robot to obtain a high-level understanding of the surrounding objects and perform context-aware decision making. Existing techniques for point cloud semantic segmentation are mostly applied on a single-frame or offline basis, with no way to integrate the segmentation results over time. This paper proposes an online method for mobile robots to incrementally build a semantically-rich 3D point cloud of the environment. The proposed deep neural network, MCPNet, is trained to predict class labels and object instance labels for each point in the scanned point cloud in an incremental fashion. A multi-view context pooling (MCP) operator is used to combine point features obtained from multiple viewpoints to improve the classification accuracy. The proposed architecture was trained and evaluated on ray-traced scans derived from the Stanford 3D Indoor Spaces dataset. Results show that the proposed approach led to 15% improvement in point-wise accuracy and 7% improvement in NMI compared to the next best online method, with only a 6% drop in accuracy compared to the PointNet-based offline approach.
FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
Venue: arXiv cs.CV 2019
Year: 2019

Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset.
The Complexity of Max-Min $k$-Partitioning
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we study a max-min $k$-partition problem on a weighted graph, that could model a robust $k$-coalition formation. We settle the computational complexity of this problem as complete for class $Σ_2^P$. This hardness holds even for $k=2$ and arbitrary weights, or $k=3$ and non-negative weights, which matches what was known on \textsc{MaxCut} and \textsc{Min-3-Cut} one level higher in the polynomial hierarchy.
Low-bit Quantization of Neural Networks for Efficient Inference
Venue: arXiv cs.LG 2019
Year: 2019

Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks. The gains in performance come at the cost of a substantial increase in computation and storage requirements. This makes real-time implementations on limited resources hardware a challenging task. One popular approach to address this challenge is to perform low-bit precision computations via neural network quantization. However, aggressive quantization generally entails a severe penalty in terms of accuracy, and often requires retraining of the network, or resorting to higher bit precision quantization. In this paper, we formalize the linear quantization task as a Minimum Mean Squared Error (MMSE) problem for both weights and activations, allowing low-bit precision inference without the need for full network retraining. The main contributions of our approach are the optimizations of the constrained MSE problem at each layer of the network, the hardware aware partitioning of the network parameters, and the use of multiple low precision quantized tensors for poorly approximated layers. The proposed approach allows 4 bits integer (INT4) quantization for deployment of pretrained models on limited hardware resources. Multiple experiments on various network architectures show that the suggested method yields state of the art results with minimal loss of tasks accuracy.
Commodity RGB-D Sensors: Data Acquisition
Venue: arXiv cs.CV 2019
Year: 2019

Over the past ten years we have seen a democratization of range sensing technology. While previously range sensors have been highly expensive and only accessible to a few domain experts, such sensors are nowadays ubiquitous and can even be found in the latest generation of mobile devices, e.g., current smartphones. This democratization of range sensing technology was started with the release of the Microsoft Kinect, and since then many different commodity range sensors followed its lead, such as the Primesense Carmine, Asus Xtion Pro, and the Structure Sensor from Occipital. The availability of cheap range sensing technology led to a big leap in research, especially in the context of more powerful static and dynamic reconstruction techniques, starting from 3D scanning applications, such as KinectFusion, to highly accurate face and body tracking approaches. In this chapter, we have a detailed look into the different types of existing range sensors. We discuss the two fundamental types of commodity range sensing techniques in detail, namely passive and active sensing, and we explore the principles these technologies are based on. Our focus is on modern active commodity range sensors based on time-of-flight and structured light. We conclude by discussing the noise characteristics, working ranges, and types of errors made by the different sensing modalities.
Fusing Visual, Textual and Connectivity Clues for Studying Mental Health
Venue: arXiv cs.CY 2019
Year: 2019

With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing textual content, we examine and exploit multimodal big data to discern depressive behavior using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques for fusing heterogeneous sets of features obtained by processing visual, textual and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inference from social media for broader applications. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.
Error reduction of quantum algorithms
Venue: arXiv cs.CC 2019
Year: 2019

We give a technique to reduce the error probability of quantum algorithms that determine whether its input has a specified property of interest. The standard process of reducing this error is statistical processing of the results of multiple independent executions of an algorithm. Denoting by $ρ$ an upper bound of this probability (wlog., assume $ρ\le \frac{1}{2}$), classical techniques require $O(\fracρ{[(1-ρ) - ρ]^2})$ executions to reduce the error to a negligible constant. We investigated when and how quantum algorithmic techniques like amplitude amplification and estimation may reduce the number of executions. On one hand, the former idea does not directly benefit algorithms that can err on both yes and no answers and the number of executions in the latter approach is $O(\frac{1}{(1-ρ) - ρ})$. We propose a novel approach named as {\em Amplitude Separation} that combines both these approaches and achieves $O(\frac{1}{\sqrt{1-ρ} - \sqrtρ})$ executions that betters existing approaches when the errors are high. In the Multiple-Weight Decision Problem, the input is an $n$-bit Boolean function $f()$ given as a black-box and the objective is to determine the number of $x$ for which $f(x)=1$, denoted as $wt(f)$, given some possible values $\{w_1, \ldots, w_k\}$ for $wt(f)$. When our technique is applied to this problem, we obtain the correct answer, maybe with a negligible error, using $O(\log_2 k \sqrt{2^n})$ calls to $f()$ that shows a quadratic speedup over classical approaches and currently known quantum algorithms.
There are No Bit Parts for Sign Bits in Black-Box Attacks
Venue: arXiv cs.LG 2019
Year: 2019

We present a black-box adversarial attack algorithm which sets new state-of-the-art model evasion rates for query efficiency in the $\ell_\infty$ and $\ell_2$ metrics, where only loss-oracle access to the model is available. On two public black-box attack challenges, the algorithm achieves the highest evasion rate, surpassing all of the submitted attacks. Similar performance is observed on a model that is secure against substitute-model attacks. For standard models trained on the MNIST, CIFAR10, and IMAGENET datasets, averaged over the datasets and metrics, the algorithm is 3.8x less failure-prone, and spends in total 2.5x fewer queries than the current state-of-the-art attacks combined given a budget of 10, 000 queries per attack attempt. Notably, it requires no hyperparameter tuning or any data/time-dependent prior. The algorithm exploits a new approach, namely sign-based rather than magnitude-based gradient estimation. This shifts the estimation from continuous to binary black-box optimization. With three properties of the directional derivative, we examine three approaches to adversarial attacks. This yields a superior algorithm breaking a standard MNIST model using just 12 queries on average!
Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
Venue: arXiv cs.CV 2019
Year: 2019

Environmental air quality affects people's life, obtaining real-time and accurate environmental air quality has a profound guiding significance for the development of social activities. At present, environmental air quality measurement mainly adopts the method that setting air quality detector at specific monitoring points in cities and timing sampling analysis, which is easy to be restricted by time and space factors. Some air quality measurement algorithms related to deep learning mostly adopt a single convolutional neural network to train the whole image, which will ignore the difference of different parts of the image. In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images. Our method mainly includes two aspects: ensemble learning of double-channel convolutional neural network and self-learning weighted feature fusion. We constructed a double-channel convolutional neural network, used each channel to train different parts of the environment images for feature extraction. We propose a feature weight self-learning method, which weights and concatenates the extracted feature vectors, and uses the fused feature vectors to measure air quality. Our method can be applied to the two tasks of air quality grade measurement and air quality index (AQI) measurement. Moreover, we build an environmental image dataset of random time and location condition. The experiments show that our method can achieve nearly 82% accuracy and a small mean absolute error (MAE) on our test dataset. At the same time, through comparative experiment, we proved that our proposed method gained considerable improvement in performance compared with single channel convolutional neural network air quality measurements.
Wireless Key Generation from Imperfect Channel State Information: Performance Analysis and Improvements
Venue: arXiv cs.IT 2019
Year: 2019

The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).
Classifying textual data: shallow, deep and ensemble methods
Venue: arXiv cs.CL 2019
Year: 2019

This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. The study is motivated by a challenging real data problem, characterized by high-dimensional and extremely sparse data, deriving from incoming calls to the customer care of an Italian phone company. We will show that deep learning outperforms many classical (shallow) strategies but the combination of shallow and deep learning methods in a unique ensemble classifier may improve the robustness and the accuracy of "single" classification methods.
Estimating Network Effects Using Naturally Occurring Peer Notification Queue Counterfactuals
Venue: arXiv cs.SI 2019
Year: 2019

Randomized experiments, or A/B tests are used to estimate the causal impact of a feature on the behavior of users by creating two parallel universes in which members are simultaneously assigned to treatment and control. However, in social network settings, members interact, such that the impact of a feature is not always contained within the treatment group. Researchers have developed a number of experimental designs to estimate network effects in social settings. Alternatively, naturally occurring exogenous variation, or 'natural experiments,' allow researchers to recover causal estimates of peer effects from observational data in the absence of experimental manipulation. Natural experiments trade off the engineering costs and some of the ethical concerns associated with network randomization with the search costs of finding situations with natural exogenous variation. To mitigate the search costs associated with discovering natural counterfactuals, we identify a common engineering requirement used to scale massive online systems, in which natural exogenous variation is likely to exist: notification queueing. We identify two natural experiments on the LinkedIn platform based on the order of notification queues to estimate the causal impact of a received message on the engagement of a recipient. We show that receiving a message from another member significantly increases a member's engagement, but that some popular observational specifications, such as fixed-effects estimators, overestimate this effect by as much as 2.7x. We then apply the estimated network effect coefficients to a large body of past experiments to quantify the extent to which it changes our interpretation of experimental results. The study points to the benefits of using messaging queues to discover naturally occurring counterfactuals for the estimation of causal effects without experimenter intervention.
PIT for depth-$4$ circuits and Sylvester-Gallai conjecture for polynomials
Venue: arXiv cs.CC 2019
Year: 2019

This text is a development of a preprint of Ankit Gupta. We present an approach for devising a deterministic polynomial time blackbox identity testing (PIT) algorithm for depth-$4$ circuits with bounded top fanin. This approach is similar to Kayal-Shubhangi approach for depth-$3$ circuits. Kayal and Shubhangi based their algorithm on Sylvester-Gallai-type theorem about linear polynomials. We show how it is possible to generalize this approach to depth-$4$ circuits. However we failed to implement this plan completely. We succeeded to construct a polynomial time deterministic algorithm for depth-$4$ circuits with bounded top fanin and its correctness requires a hypothesis. Also we present a polynomial-time (unconditional) algorithm for some subclass of depth-$4$ circuits with bounded top fanin.
Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines under Unknown Measurement Noise Statistics
Venue: arXiv cs.SY 2019
Year: 2019

Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviatefrom the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.
Uniform Substitution At One Fell Swoop
Venue: arXiv cs.LO 2019
Year: 2019

Uniform substitution of function, predicate, program or game symbols is the core operation in parsimonious provers for hybrid systems and hybrid games. By postponing soundness-critical admissibility checks, this paper introduces a uniform substitution mechanism that proceeds in a linear pass homomorphically along the formula. Soundness is recovered using a simple variable condition at the replacements performed by the substitution. The setting in this paper is that of differential hybrid games, in which discrete, continuous, and adversarial dynamics interact in differential game logic dGL. This paper proves soundness and completeness of one-pass uniform substitutions for dGL.
When Energy Trading meets Blockchain in Electrical Power System: The State of the Art
Venue: arXiv cs.CR 2019
Year: 2019

With the rapid growth of renewable energy resources, the energy trading began to shift from centralized to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted to design new energy trading schemes. However, there are many challenging issues for blockchain-based energy trading, i.e., low efficiency, high transaction cost, security & privacy issues. To tackle with the above challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified. Then, the existing energy trading schemes are studied and classified into three categories based on their main focus: energy transaction, consensus mechanism, and system optimization. And each category is presented in detail. Although existing schemes can meet the specific energy trading requirements, there are still many unsolved problems. Finally, the discussion and future directions are given.